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Ableton Producer MCP

Ableton Producer MCP

by kovtun-oleksii
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

record_audition

Log browser, preset, or sample auditions as kept or rejected so preference-learning data can guide future Ableton searches and selections.

Instructions

Log a browser/preset/sample audition (keep or reject) for preference learning.

Call once per candidate auditioned. Does not load the device — use load_instrument_or_effect when kept=True and you want to commit.

Requires telemetry consent.

Parameters:

  • uri: Browser item URI (or stable preset/sample id)

  • kept: Whether this candidate was kept

  • search_query: Optional search text that led here (e.g. "analog bass")

  • dwell_ms: Optional time spent auditioning

  • user_prompt: The original user prompt that led to this tool call (for telemetry)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "uri": "<uri>"
    },
    "name": "record_audition"
  }
}

Input Schema

NameRequiredDescriptionDefault
uriYes
keptNo
dwell_msNo
user_promptNo
search_queryNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.5+automation.1

TDQS

A4.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavior, and it discloses meaningful side-effect boundaries: it logs/records for telemetry and preference learning, does not load the device, and requires telemetry consent. It does not specify failure behavior when consent is absent or how logged data is retained, so it is not fully exhaustive, but it is transparent about the main side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is tightly organized: a one-sentence purpose, two critical behavioral directives, and a compact parameter list. No sentence is redundant; each line carries operational information, and the most decision-relevant facts are front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-parameter tool with no annotations, this is complete: it covers purpose, calling cadence, consent prerequisite, side-effect boundary, the relationship to load_instrument_or_effect, and the meaning of each parameter. Since an output schema is present, return-value details do not need to be repeated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description compensates completely with a parameter list that adds meaning beyond the bare schema titles: uri is described as 'Browser item URI (or stable preset/sample id)', search_query includes a concrete example, and user_prompt explains its telemetry purpose. Every parameter receives useful semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action and scope: 'Log a browser/preset/sample audition (keep or reject) for preference learning.' This clearly identifies the resource and purpose, and the sentence 'Does not load the device' explicitly separates it from the sibling load_instrument_or_effect. An agent can tell what this tool is for without needing to inspect the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a clear call policy ('Call once per candidate auditioned'), a prerequisite ('Requires telemetry consent'), and an explicit when-not-to-use with an alternative ('Does not load the device — use load_instrument_or_effect when kept=True and you want to commit'). This is strong routing guidance beyond what the schema provides.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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